System and method for normalizing dynamic range of data acquired utilizing medical imaging

ABSTRACT

A computer-implemented method for image processing is provided. The method includes obtaining data acquired by a medical imaging system. The method also includes normalizing the data. The method further includes de-noising the normalized data utilizing a deep learning-based denoising network. The method even further includes de-normalizing the de-noised data. The method yet further includes generating blended data based on both the data and the de-normalized de-noised data.

BACKGROUND

The subject matter disclosed herein relates to imaging systems and, moreparticularly, to techniques for processing data (e.g., projection orimage data) acquired utilizing medical imaging or other imaging systemsthat encounter huge dynamic ranges in their measurements.

Non-invasive imaging technologies allow images of the internalstructures or features of a subject (patient, manufactured good,baggage, package, or passenger) to be obtained non-invasively. Inparticular, such non-invasive imaging technologies rely on variousphysical principles, such as the differential transmission of X-raysthrough the target volume or the reflection of acoustic waves, toacquire data and to construct images or otherwise represent the internalfeatures of the subject.

For example, in X-ray-based imaging technologies, a subject of interest,such as a human patient, is irradiated with X-ray radiation and theattenuated radiation impacts a detector where the attenuated intensitydata is collected. In digital X-ray systems, a detector produces signalsrepresentative of the amount or intensity of radiation impactingdiscrete pixel regions of a detector surface. The signals may then beprocessed to generate an image that may be displayed for review.

In one such X-ray based technique, known as computed tomography (CT), ascanner may project fan-shaped or cone-shaped X-ray beams from an X-raysource from numerous view-angle positions on an object being imaged,such as a patient. The X-ray beams are attenuated as they traverse theobject and are detected by a set of detector elements which producesignals representing the intensity of the attenuated X-ray radiation onthe detector. The signals are processed to produce data representing theline integrals of the linear attenuation coefficients of the objectalong the X-ray paths. These signals are typically called “projectiondata” or just “projections”. By using reconstruction techniques, such asfiltered backprojection, images may be generated that represent a volumeor a volumetric rendering of a region of interest of the patient orimaged object. In a medical context, pathologies or other structures ofinterest may then be located or identified from the reconstructed imagesor rendered volume.

Noise in the sinogram domain of the acquired CT data may result inundesirable artifacts. These artifacts may include streaks and/orheavy-tailed image noise in reconstructed images. In addition, noise inthe sinogram domain may lead to non-positive measurements, inparticular, when the object being scanned is highly attenuating innature. However, under-correction of non-positive measurements may leadto bright streaks, while over-correction can cause dark bands to appearin the images. Further, the noise in the sinogram domain that results inthe streak and heavy-tailed noise in the reconstructed images may bedifficult to reduce utilizing techniques that are applied in the imagedomain.

BRIEF DESCRIPTION

Certain embodiments commensurate in scope with the originally claimedsubject matter are summarized below. These embodiments are not intendedto limit the scope of the claimed subject matter, but rather theseembodiments are intended only to provide a brief summary of possibleforms of the subject matter. Indeed, the subject matter may encompass avariety of forms that may be similar to or different from theembodiments set forth below.

In accordance with an embodiment, a computer-implemented method forimage processing is provided. The method includes obtaining dataacquired by a medical imaging system. The method also includesnormalizing the data. The method further includes de-noising thenormalized data utilizing a deep learning-based denoising network. Themethod even further includes de-normalizing the de-noised data. Themethod yet further includes generating blended data based on both thedata and the de-normalized de-noised data.

In accordance with another embodiment, one or more non-transitorycomputer-readable media are provided. The computer-readable media encodeone or more processor-executable routines. The one or more routines,when executed by a processor, cause acts to be performed. The actsinclude obtaining projection data acquired by a computed tomography (CT)imaging system and normalizing the projection data. The acts alsoinclude de-noising the normalized projection data utilizing a deeplearning-based denoising network, de-normalizing the de-noisedprojection data, and generating blended projection data based on boththe projection data and the de-normalized de-noised projection data.

In accordance with a further embodiment, a processor-based system isprovided. The processor-based system includes a memory structureencoding one or more processor-executable routines. The routines, whenexecuted cause acts to be performed. The acts include obtainingpre-logarithm projection data acquired by a computed tomography (CT)imaging system and normalizing the pre-logarithm projection data toreduce a dynamic range of the pre-logarithm projection data. The actsalso include de-noising the normalized projection data utilizing a deeplearning-based denoising network, de-normalizing the de-noisedprojection data, and generating blended projection data based on boththe projection data and the de-normalized de-noised projection data. Theprocessor-based system also includes a processing component configuredto access and execute the one or more routines encoded by the memorystructure.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features, aspects, and advantages of the disclosedsubject matter will become better understood when the following detaileddescription is read with reference to the accompanying drawings in whichlike characters represent like parts throughout the drawings, wherein:

FIG. 1 is a schematic illustration of an embodiment of a computedtomography (CT) system configured to acquire CT images of a patient andprocess the images, in accordance with aspects of the presentdisclosure;

FIGS. 2A and 2B are schematic illustrations of an embodiment of amulti-stage approach for measured data and image processing,respectively, utilizing the disclosed techniques or methods, inaccordance with aspects of the present disclosure;

FIGS. 3A-3F illustrate a portion of a sinogram of a patientcorresponding to one detector row under a variety of conditions, inaccordance with aspects of the present disclosure;

FIGS. 4A-4C illustrate a normalized noisy measured projection,DL-denoised normalized projection, and a denoised and adaptivelycombined output (shown in a normalized domain), respectively, inaccordance with aspects of the present disclosure;

FIG. 5 illustrates a schematic diagram of an embodiment of anencoder-decoder DL network for denoising, in accordance with aspects ofthe present disclosure;

FIGS. 6A and 6B illustrate training samples for the encoder-decoder DLnetwork of FIG. 5 , in accordance with aspects of the presentdisclosure;

FIG. 7 illustrates a representation of a graph for illustrating trainingand validation loss over epochs utilizing the encoder-decoder DL networkin FIG. 5 , in accordance with aspects of the present disclosure;

FIGS. 8A and 8B illustrate axial reconstructed axial images of ashoulder region of a subject, in accordance with aspects of the presentdisclosure;

FIGS. 9A and 9B illustrate axial reconstructed CT images of a pelvicregion of a subject, in accordance with aspects of the presentdisclosure; and

FIGS. 10A and 10B illustrate reconstructed coronal images of anabdominal region of a subject, in accordance with aspects of the presentdisclosure.

DETAILED DESCRIPTION

One or more specific embodiments will be described below. In an effortto provide a concise description of these embodiments, all features ofan actual implementation may not be described in the specification. Itshould be appreciated that in the development of any such actualimplementation, as in any engineering or design project, numerousimplementation-specific decisions must be made to achieve thedevelopers' specific goals, such as compliance with system-related andbusiness-related constraints, which may vary from one implementation toanother. Moreover, it should be appreciated that such a developmenteffort might be complex and time consuming, but would nevertheless be aroutine undertaking of design, fabrication, and manufacture for those ofordinary skill having the benefit of this disclosure.

When introducing elements of various embodiments of the present subjectmatter, the articles “a,” “an,” “the,” and “said” are intended to meanthat there are one or more of the elements. The terms “comprising,”“including,” and “having” are intended to be inclusive and mean thatthere may be additional elements other than the listed elements.Furthermore, any numerical examples in the following discussion areintended to be non-limiting, and thus additional numerical values,ranges, and percentages are within the scope of the disclosedembodiments.

While aspects of the following discussion may be provided in the contextof medical imaging, it should be appreciated that the present techniquesare not limited to such medical contexts. Indeed, the provision ofexamples and explanations in such a medical context is only tofacilitate explanation by providing instances of real-worldimplementations and applications. However, the present approaches mayalso be utilized in other contexts, such as imaging (e.g., in industrialuse) in non-destructive inspection of manufactured parts or goods (i.e.,quality control or quality review applications), and/or the non-invasiveinspection of packages, boxes, luggage, and so forth (i.e., security orscreening applications), and generally in imaging applications thatencounter high dynamic range measurements such as fluorescencemicroscopy and astronomy imaging.

Deep-learning (DL) approaches discussed herein may be based onartificial neural networks, and may therefore encompass one or more ofdeep neural networks, fully connected networks, convolutional neuralnetworks (CNNs), perceptrons, encoders-decoders, recurrent networks,u-nets, generative adversarial networks (GANs), or other neural networkarchitectures. The neural networks may include shortcuts, activations,batch-normalization layers, and/or other features. These techniques arereferred to herein as deep-learning techniques, though this terminologymay also be used specifically in reference to the use of deep neuralnetworks, which is a neural network having a plurality of layers.

As discussed herein, deep-learning techniques (which may also be knownas deep machine learning, hierarchical learning, or deep structuredlearning) are a branch of machine learning techniques that employmathematical representations of data and artificial neural networks forlearning and processing such representations. By way of example,deep-learning approaches may be characterized by their use of one ormore algorithms to extract or model high level abstractions of a type ofdata-of-interest. This may be accomplished using one or more processinglayers, with each layer typically corresponding to a different level ofabstraction and, therefore potentially employing or utilizing differentaspects of the initial data or outputs of a preceding layer (i.e., ahierarchy or cascade of layers) as the target of the processes oralgorithms of a given layer. In an image processing or reconstructioncontext, this may be characterized as different layers corresponding tothe different feature levels or resolution in the data. In general, theprocessing from one representation space to the next-levelrepresentation space can be considered as one ‘stage’ of the process.Each stage of the process can be performed by separate neural networksor by different parts of one larger neural network.

Noise in the sinogram domain (e.g., in the projection data of thecomputed tomography data acquired with a CT scanner) may result inundesirable artifacts in reconstructed images. In addition, noise in thesinogram domain may lead to non-positive measurements (i.e., low signal)due to a scanned object being highly attenuating in nature. However,correction (e.g., under-correction or over-correction) of thenon-positive measurements (e.g., before the negative-logarithm stepduring reconstruction) may lead to bright streaks and/or dark bands inthe reconstructed images. Correction of these undesirable artifacts inthe reconstructed images is difficult utilizing techniques that areapplied to the image domain alone. It is therefore desirable to correctthese artifacts utilizing techniques that reduce noise in the sinogramdomain. However, reduction of noise in the sinogram domain (inparticular, if applied to pre-logarithm projection measurements) may bedifficult due to a huge dynamic range resulting from the exponentialnature of X-ray attenuation through the object being imaged.

The present disclosure provides for methods and systems to reduce noiseand artifacts in images acquired utilizing medical imaging. Inparticular, data (e.g., projection data (pre-logarithm orpost-logarithm) or image data) is obtained that was acquired by amedical imaging system (e.g., CT imaging system, PET imaging system,etc.) and subjected to normalization. The normalized data is thende-noised utilizing a deep learning-based denoising network (e.g.,formed of one or more networks). The de-noised data is thende-normalized (e.g., by reversing the mathematical operations utilizedin normalization). Blended data is generated based on both the data andthe de-normalized de-noised data, which may be utilized in generating afinal reconstructed image with fewer artifacts. When applied toprojection data, the disclosed techniques provide both low signalcorrection and general sinogram noise reduction.

With the foregoing discussion in mind, FIG. 1 illustrates an embodimentof an imaging system 10 for acquiring and processing image data inaccordance with aspects of the present disclosure. Although thefollowing embodiments are discussed in terms of the computed tomography(CT) imaging system, the embodiments may also be utilized with otherimaging systems (e.g., X-ray, PET, CT/PET, SPECT, nuclear CT, magneticresonance imaging, etc.). In addition, the embodiments may be utilizedin any imaging applications that encounter high dynamic rangemeasurements such as fluorescence microscopy and astronomy imaging. Inthe illustrated embodiment, system 10 is a CT system designed to acquireX-ray projection data, to reconstruct the projection data into atomographic image, and to process the image data for display andanalysis. The CT imaging system 10 includes an X-ray source 12. Asdiscussed in detail herein, the source 12 may include one or more X-raysources, such as an X-ray tube or solid-state emission structures. TheX-ray source 12, in accordance with present embodiments, is configuredto emit an X-ray beam 20 at one or more energies. For example, the X-raysource 12 may be configured to switch between relatively low energypolychromatic emission spectra (e.g., at about 80 kVp) and relativelyhigh energy polychromatic emission spectra (e.g., at about 140 kVp). Aswill be appreciated, the X-ray source 12 may also be operated so as toemit X-rays at more than two different energies, though dual-energyembodiments are discussed herein to simplify explanation. Similarly, theX-ray source 12 may emit at polychromatic spectra localized aroundenergy levels (i.e., kVp ranges) other than those listed herein. Indeed,selection of the respective energy levels for emission may be based, atleast in part, on the anatomy being imaged and the chemical or moleculesof interest for tissue characterization.

In certain implementations, the source 12 may be positioned proximate toa collimator 22 used to define the size and shape of the one or moreX-ray beams 20 that pass into a region in which a subject 24 (e.g., apatient) or object of interest is positioned. The subject 24 attenuatesat least a portion of the X-rays. Resulting attenuated X-rays 26 impacta detector array 28 formed by a plurality of detector elements. Eachdetector element produces an electrical signal that represents theintensity of the X-ray beam incident at the position of the detectorelement when the beam strikes the detector 28. Electrical signals areacquired and processed to generate one or more scan datasets. Thedetector array 28 may be part of a detector that operates in anenergy-integrating (i.e., readout of the total integrated energydeposited during an acquisition interval) mode or detector that operatesin a photon-counting (each individual X-ray photon is detected and itsenergy characterized) mode.

A system controller 30 commands operation of the imaging system 10 toexecute examination and/or calibration protocols and to process theacquired data. With respect to the X-ray source 12, the systemcontroller 30 furnishes power, focal spot location, control signals andso forth, for the X-ray examination sequences. The detector 28 iscoupled to the system controller 30, which commands acquisition of thesignals generated by the detector 28. In addition, the system controller30, via a motor controller 36, may control operation of a linearpositioning subsystem 32 and/or a rotational subsystem 34 used to movecomponents of the imaging system 10 and/or the subject 24. The systemcontroller 30 may include signal processing circuitry and associatedmemory circuitry. In such embodiments, the memory circuitry may storeprograms, routines, and/or encoded algorithms executed by the systemcontroller 30 to operate the imaging system 10, including the X-raysource 12, and to process the data acquired by the detector 28 inaccordance with the steps and processes discussed herein. In oneembodiment, the system controller 30 may be implemented as all or partof a processor-based system such as a general purpose orapplication-specific computer system.

The source 12 may be controlled by an X-ray controller 38 containedwithin the system controller 30. The X-ray controller 38 may beconfigured to provide power and timing signals to the source 12. Inaddition, in some embodiments the X-ray controller 38 may be configuredto selectively activate the source 12 such that tubes or emitters atdifferent locations within the system 10 may be operated in synchronywith one another or independent of one another. In certain embodiments,the X-ray controller 38 may be configured to provide fast-kVp switchingof the X-ray source 12 so as to rapidly switch the source 12 to emitX-rays at the respective polychromatic energy spectra in successionduring an image acquisition session. For example, in a dual-energyimaging context, the X-ray controller 38 may operate the X-ray source 12so that the X-ray source 12 alternately emits X-rays at the twopolychromatic energy spectra of interest, such that adjacent projectionsare acquired at different energies (i.e., a first projection is acquiredat high energy, the second projection is acquired at low energy, thethird projection is acquired at high energy, and so forth). In one suchimplementation, the fast-kVp switching operation performed by the X-raycontroller 38 yields temporally registered projection data.

The system controller 30 may include a data acquisition system (DAS) 40.The DAS 40 receives data collected by readout electronics of thedetector 28, such as sampled analog signals from the detector 28. TheDAS 40 may then convert the data to digital signals for subsequentprocessing by a processor-based system, such as a computer 42. In otherembodiments, the detector 28 may convert the sampled analog signals todigital signals prior to transmission to the data acquisition system 40.The computer may include processing circuitry 44 (e.g., image processingcircuitry). The computer 42 may include or communicate with one or morenon-transitory memory devices 46 that can store data processed by thecomputer 42, data to be processed by the computer 42, or instructions tobe executed by a processor (e.g., processing circuitry 44) of thecomputer 42. For example, the processing circuitry 44 of the computer 42may execute one or more sets of instructions stored on the memory 46,which may be a memory of the computer 42, a memory of the processor,firmware, or a similar instantiation. In accordance with presentembodiments, the memory 46 stores sets of instructions that, whenexecuted by the processor, perform image processing methods as discussedherein. The memory 46 also stores one or more algorithms and/or neuralnetworks 47 that may be utilized in denoising as described in greaterdetail below.

The computer 42 may also be adapted to control features enabled by thesystem controller 30 (i.e., scanning operations and data acquisition),such as in response to commands and scanning parameters provided by anoperator via an operator workstation 48. The system 10 may also includea display 50 coupled to the operator workstation 48 that allows theoperator to view relevant system data, imaging parameters, raw imagingdata, reconstructed data, contrast agent density maps produced inaccordance with the present disclosure, and so forth. Additionally, thesystem 10 may include a printer 52 coupled to the operator workstation48 and configured to print any desired measurement results. The display50 and the printer 52 may also be connected to the computer 42 directlyor via the operator workstation 48. Further, the operator workstation 48may include or be coupled to a picture archiving and communicationssystem (PACS) 54. PACS 54 may be coupled to a remote system 56,radiology department information system (RIS), hospital informationsystem (HIS) or to an internal or external network, so that others atdifferent locations can gain access to the image data.

Sinograms are created by arranging the projection data with projectionangles arranged along one axis and the spatial dimensions of theprojection data arranged along the other axes. Each row of a sinogramconstitutes a projection view that is indicative of the attenuationinformation for a distinct view angle, for given source and detectorpositions, with respect to a subject or object imaged. Low signalcorrection (LSC) is an important sinogram processing step utilized toeliminate or reduce non-positive measurements before the negative-logoperation. LSC also aims to improve signal-to-noise ratio (SNR) inregions of the sinogram where X-rays have been heavily attenuated. Boththese scenarios typically correspond to photon starvation that arisewhen X-rays are heavily attenuated (due to metal, contrast agent and/orlarge objects in the X-ray path). The low signal regions may span manyconsecutive detector elements across many views thus necessitating aDL-based denoising algorithm that can denoise large swathes of thesinogram. However, lack of correction or poor correction may lead toheavy streaks and possibly heavy-tailed noise in the reconstructed imagevolume. In dual-energy CT (DECT), photon starvation can occur in thelow-kV sinogram, while in multiple-energy spectral CT (MECT), it canoccur in energy bins that have relatively less signal inenergy-integrating detectors or low counts in photon-counting detectors.Material decomposition, which combines DECT or MECT sinograms to obtainmaterial sinograms or images, further worsens the scenario as streaksare bound to spread to all material images from the photon-starvedsinograms. Therefore, LSC becomes important for DECT and MECT as well.

Moreover, increased sinogram noise tends to produce undesirable features(e.g., streaks and possibly heavy-tailed noise) in the reconstructedimage volume. These undesired artifacts are not easily reduced by imagedomain denoising algorithms alone, because these artifacts are stronglycorrelated spatially. For example, streaks are corrected along theirdirection of orientation and heavy-tailed noise-residue may beinterpreted as features-of-interest and may, thus, be preserved byimage-domain denoising methods. However, noise in the sinogram domain isspatially uncorrelated and is easier to denoise. DL denoising methodshave been demonstrated to outperform state-of-the-art analyticaldenoising methods. Thus, the following focuses on DL-based LSC andsinogram denoising as a means of improving image quality insingle-energy CT (SECT), DECT and MECT.

Application of DL denoising directly to pre-logarithm projectionmeasurements poses a challenge. Pre-logarithm projection measurementshave a huge dynamic range due the exponential nature of attenuation ofX-rays through objects. Training of DL networks to handle such largedynamic range can lead to numerical difficulties (especially consideringthat the training process involves highly non-convex optimization) andcan lead to sub-optimally trained networks that fall short of theirintended goal. Both inter-object (object to object) and intra-object(spatial variation within the object) variation of the dynamic range cancause numerical instabilities during training and application of DLnetworks for sinogram denoising. A workaround is to partition the hugedynamic range and train multiple networks, one for each partition.However, this approach requires training of multiple networks and doesnot eliminate the inter-object and intra-object dependencies.

Disclosed herein is a multi-stage approach (e.g., 3-stage approach) toDL-based LSC and sinogram denoising as illustrated in FIG. 2A. The firststage is a pre-processing stage 58 that normalizes the projectionmeasurements and compresses the dynamic range irrespective of the objectbeing studied. The second stage is the actual DL LSC and denoising stage60 where a DL network takes the normalized projection measurements andoutputs a denoised version. The final stage 62 undoes the normalization(via de-normalization) and adaptively combines the denoised output withthe original measurements to reduce any resolution loss that may occuras a result of denoising. In certain embodiments, the projectionmeasurements may be pre-logarithm projection measurements. In otherembodiments, the projection measurements may be post-logarithmprojection measurements. In certain embodiments, the multi-stageapproach may be applied to image data as depicted in FIG. 2B. Inparticular, the image data may be normalized, denoised via a DL network,denormalized, and then the denormalized denoised image data may beadaptively combined with the original image data.

The individual stages of the multi-stage approach are described ingreater detail below with pre-logarithm projection measurements from CTutilized as an example. Similar approaches may be taken withpost-logarithm projection measurements and image data.

Normalization

Noise in pre-logarithm projection measurements can be fairly accuratelymodeled as Poisson in photon-counting detectors or approximately modeledas a Poisson-Gaussian mixture in energy-integrating detectors. Bothmodels result in spatially varying levels of noise, with low signalregions having poorer SNR compared to high signal regions. Therefore, itis preferable that the normalization highlights or focuses on the noisein poor SNR regions.

Noting that low-frequency components of pre-logarithm projectionmeasurements mainly contribute to their dynamic range, as part of thenormalization, the described techniques focus on separating thelow-frequency components from the measurements. The low-frequencycomponents may be separated from the measurement in a couple ofdifferent ways: (i) by subtracting the low-frequency components from themeasurements to yield only high-frequency components or (ii) byobtaining a ratio of the measurements to the low-frequency components.Option (i) highlights both noise and edges in the entire sinogram, whileoption (ii) increases noise at low SNR regions and subdues noise at highSNR regions naturally highlighting the noisy regions for DL to denoise.Based on these two observations, the following normalization strategy isprovided.

Let P denote the projection measurements. Then, the normalizationprocess involves first obtaining a preliminary noise-reduced version,P_(d), of P by applying off-the-shelf algorithms (e.g., a simplesmoothing-filter or in a more sophisticated case, a bilateral-likefilter). Care is taken to ensure that P_(d) is positive, i.e., P_(d)>0.Then, the normalized projection, P_(n), is obtained as

$\begin{matrix}{{P_{n} = \frac{P - {g(P)}}{f\left( P_{d} \right)}},} & (1)\end{matrix}$

where g is a function that produces a noise-reduced version of P (e.g.,via low-pass filtering or conventional denoising), and f is a positivemonotonic function. Monotonicity of f is preferable for the above ratioto maintain edge-information in the normalized projection, P_(n), thatis consistent with the edge-information in the measured projection P.The normalization strategy in equation (1) captures both options (i) and(ii) discussed above for the respective special cases when f(P_(d))=1(i.e., do not divide by f(P_(d))) and when g(P)=0, respectively. Notethat g(P) and P_(d) can be two different noise-reduced versions of P.

FIGS. 3A-3F illustrate a portion of a sinogram of a patientcorresponding to one detector row under of a variety of conditions. FIG.3A illustrates a pre-logarithm projection measurements of the sinogramhaving a dynamic range between −6×10³ and 7×10⁶ counts. Due to this hugedynamic range only a portion of the sinogram is shown. FIG. 3Billustrates an estimated SNR in the pre-logarithm projectionmeasurements with a dynamic range of −8 to 51 decibels (dB). FIG. 3Cillustrates an air-scan normalized, clipped post-logarithm projectiondata with a dynamic range between 0 and 13 dB. This range is still highenough that only a portion of the sinogram can be displayed while tryingto highlight low signal regions. FIG. 3D illustrates a high-passcomponent, P−g(P), of the pre-logarithm projection measurements with adynamic range between −2.5×10⁶ and 2.5×10⁶ counts. Due to this hugedynamic range, it is difficult to uniformly highlight noise in the wholesinogram. FIG. 3E illustrates one version of the disclosed normalizedsinogram derived utilizing P/ƒ(P_(d)) with a dynamic range of −0.6 to3.6. FIG. 3F is another version of the disclosed normalized sinogramderived utilizing equation (1) with a dynamic range between −2 and 2.5.

The effect of the disclosed normalization strategy in equation (1) canbe compared with the cases only looking at the high-frequency componentand the standard approach of taking the logarithm to compress dynamicrange. The disclosed normalization strategy in equation (1) compressesthe dynamic range and acts as a noise-level indicator by highlightingregions of high noise. This is because in low signal regions, themagnitude of the denominator in equation (1) is low, which thushighlights noise in low SNR regions. In contrast, the high SNR regionsin the normalized sinograms in FIGS. 3E and 3F have lower noise. Thus,equation (1) essentially acts as an indicator of the noise-level of thepre-logarithm projection measurements.

In addition, the proposed normalization strategy in equation (1) may beutilized as an edge indicator to highlight edges by designing P_(d) andƒ so that the denominator in equation (1) behaves slightly differentthan the numerator around edges. For example, if P_(d) a smoothedversion of P, then equation (1) will naturally highlight edges asillustrated in FIGS. 3E and 3F. If P_(d) is an edge-preserving denoiser,then edges in the normalized output will be subdued since those edgeswill have already been captured by P_(d).

The solid arrow in FIGS. 3A and 3B indicate low signal regions and lowSNR regions, respectively. The noise present in these low signal regionsis not apparent (as indicated by the hollow arrow) in the post-logarithmprojection data in FIG. 3C even though the dynamic range is compressed.In addition, the edge details are not as enhanced in FIG. 3C compared toFIGS. 3E and 3F (which correspond to the disclosed normalizationschemes). Even though the high-pass component in FIG. 3D highlightsedges, the dynamic range is still huge and the noise in the low signalregion (as indicated by the hollow arrow) is not apparent. The disclosednormalization schemes utilized in FIGS. 3E and 3F compress the dynamicrange, highlight edges, and brings out noise in the low signals regions(as indicated by the solid arrows). The function g was implemented as a11×11 boxcar smoothing filter and ƒ(P_(d))=P_(d), where P_(d) wasobtained by smoothing P with a 15×15 boxcar filter. The differencebetween FIGS. 3E and 3F is minimal with FIG. 3F highlighting the edgesslightly more but the noise being of similar levels in FIGS. 3E and 3F.

Noise may be modulated via ƒ. In the simplest case of identity mapping,ƒ(P_(d))=P_(d), with P_(d)>0, and ignoring g(P) in equation (1),resulting in P_(n)=P/P_(d), which yields a normalized sinogram (such asin FIG. 3E), P_(n), that is approximately in the range [1−ε, 1+ε], whereε<1, when P_(d)>>0, irrespective of the object being imaged. However,noise in the low signal regions in the normalized sinogram can furtherbe modulated by choosing how the function ƒ(x) behaves for x→0.Obviously, care should be taken so that ƒ(x) does not become close tozero to avoid noise-blowup in low-signal regions. ƒ can be designed suchthat ƒ(x)∈[a, b] for x→0, where 0<a<b and ƒ(x)≈x elsewhere, whilechoosing a and b to ensure that the ratio in equation (1) stays withincertain acceptable limits in the low signal regions. For instance, itcan be designated that

$\begin{matrix}{{f(x)} = \frac{a + x^{2}}{1 + x}} & \end{matrix}$so that for x→0, ƒ(x)→a and for x>>0, ƒ(x)→x.

In summary, normalization compresses the dynamic range, whilehighlighting noisy regions and edges in the projections to make itfeasible for a DL-network to preserve edges during denoising. With thedisclosed normalization it is therefore feasible to denoise entireprojection measurements, independent of the object being scanned, usinga DL network.

DL-Denoising

Any type of DL denoising network, such those available from imagedenoising literature, can then be employed for DL denoising in theproposed framework. The normalized projection is fed as input and theoutput of the DL-network is a denoised version of the normalizedprojection. Difference-based loss functions can be utilized to train theDL network such as,

$\begin{matrix}{{\sum\limits_{i}{h\left( {❘{{{DL}\left\{ P_{n}^{i,{train}} \right\}} - P_{n}^{i,{label}}}❘} \right)}},} & (2)\end{matrix}$

where h is a non-negative monotonic function, P_(n) ^(i,train) the ithtwo-dimensional (2D) or three-dimensional (3D) training patch of noisynormalized projection data input to the DL network, DL{P_(n) ^(i,train)}is the output of the network, and P_(n) ^(i,label) the correspondingnormalized ground-truth label of projection data. In certainembodiments, additional loss terms may be included in equation (2) thatdrive the DL network to train to preserve (a) projection features and(b) local mean. The former improves denoising performance, while thelatter reduces any bias due to denoising.

Post-Processing

The denoised version of the normalized sinogram, which is output fromthe DL-denoiser, undergoes a de-normalization step where thenormalization is undone by basically reversing the mathematicaloperations involved in the normalization step in equation (1), that is,the de-normalized denoised projection is given byP _(denoised)=ƒ(P _(d))×P _(n) ^(denoised) +g(P)where P_(n) ^(denoised) is the denoised normalized projection outputfrom the DL denoiser in the second stage. While DL-denoising has thepotential to reduce sinogram noise, it is still possible that sinogramentries corresponding to some fine image features may be filtered out.Because sinogram domain operations potentially impact the wholereconstructed image volume due to backprojection, it is crucial topreserve useful information from the original projection measurements.This is achieved by adaptively combining the denoised sinogram with theoriginal measured projections, that is, the final adaptively combinedoutput is given byP _(final) =w(P,P _(denoised))×P _(denoised)+(1−w(P,P_(denoised)))×P  (4).or more generally:P _(final)=ƒ(P,P _(denoised))  (4).

where F( ) may be a non-linear function of P and P_(denoised), such asbut not limited to another deep learning network.

The adaptivity or weights can be based on estimated SNR (e.g., similarto that in FIG. 3B) of the measured projections. In such a case, w(P,P_(denoised)) is small for high SNR regions or high signal strengths sothat the original measurements can be trusted more and, thus, contributemore to the final output P_(final) in equation (4). Alternatively, theweights can be based on an estimate of the noise level in the originalmeasurements P, in which case, w(P, P_(denoised)) will be high whennoise in P is high so that more trust is given to the denoised versionP_(denoised) and make it contribute more to the final output P_(final).

FIGS. 4A-4C illustrate a normalized noisy measured projection,DL-denoised normalized projection, and a denoised and adaptivelycombined output (shown in a normalized domain), respectively. Inparticular, FIG. 4B illustrates the output of the DL-denoiser. TheDL-output in FIG. 4B has reduced noise but appears to be slightlysmudged; however, after going through stage 3 (de-normalization andadaptive combination) appears in FIG. 4C to have more detail than thedenoised output in FIG. 4B but at the same time has less noise comparedto the undenoised projection in FIG. 4A. For FIG. 4C, w(P, P_(d)) wascomputed based on the signal strength in P_(denoised).

As mentioned above, the techniques described above for SECT may alsoapply to DECT and MECT as follows.

Normalization

In DECT and MECT, there are measurements corresponding to differentspectral energy ranges or energy bins, so that measurementscorresponding to some energy ranges or bins have better SNR compared toothers. Therefore, it is proposed to use high-SNR measurements fornormalization. For instance, the high-kV projections in DECT are usuallyless noisy than the low-kV ones. Thus, it is better to trust P^(high)for normalization and to perform a preliminary denoising of the high-kVprojection P^(high) to get P_(d) ^(high) and use that to normalize boththe low-kV and high-kV projections, P^(low) and P^(high) respectively,using equation (1), to get respective normalized projections as

$P_{n}^{low} = {\frac{P^{low} - {g\left( P^{low} \right)}}{f\left( P_{d}^{high} \right)}{and}}$${P_{n}^{high} = \frac{P^{high} - {g\left( P^{high} \right)}}{f\left( P_{d}^{high} \right)}},$respectively. Similarly, in MECT, the normalization can be done bycomputing the preliminary denoised version, p_(d) ^(n,MECT),corresponding to measurements from the nth energy-bin with the highestrecorded signal (either in terms of signal-energy or in terms of photoncount) or P_(d) ^(Total,MECT), corresponding to the total measuredcounts and use that to normalize measurements from all energy-bins usingequation (1).

DL-Denoising

In DECT and MECT, it is proposed to feed the normalized dual-energy ornormalized multi-energy projections corresponding to the two or multiplerespective energy bins jointly as input channels to the DL-networksimilar to joint denoising of color images with multiple color channels.The motivation is that there is spatially correlated and spectrallycomplementary information in the projections corresponding to the twoenergies in DECT or multiple energy bins of MECT that can aid thedenoising of measurements corresponding to all energies or energy bins.Denoising DECT and MECT projection data may be denoised utilizingtechniques described in U.S. patent application Ser. No. 16/505,870,entitled “System and Method for Processing Data Acquired UtilizingMulti-Energy Computed Tomography Imaging” by Ramani et al., which ishereby incorporated by reference in its entirety.

Post-Processing

The de-normalization step for DECT and MECT are straightforwardoperations similar to that in equation (3). For the adaptive combiningstep for DECT, the weights w(P, P_(denoised) ^(high)) can be computedbased on the denoised high-kV projections because the original high-kVmeasurements, P^(high), have higher SNR and, thus, P_(denoised) ^(high)will have relatively better denoised-quality than the correspondinglow-kV denoised projections P_(denoised) ^(low). Similarly, in MECT, theweights w(P, P_(denoised) ^(n,MECT)) may be computed and utilized, whereP_(denoised) ^(n,MECT) is the DL denoised version of MECT measurementsP^(n,MECT) corresponding to that energy bin n that had the highest SNRor photon counts to begin with, or in the case where total measuredcounts is used w(P, P_(denoised) ^(Total,MECT)) may be computed andutilized. The computed weights, or w(P,P_(denoised) ^(high)) and w(P,P_(denoised) ^(n,MECT)) or w(P, P_(denoised) ^(Total,MECT)),respectively, will be used for adaptively combining low and high-kVprojections in DECT and projections from all energy bins in MECT. Thiswill ensure that the projections in DECT and MECT are treated in aspatially consistently manner during the adaptive combining step, whichis important to avoid streaks and azimuthal blur in the resultingreconstructed DECT and MECT images.

Additionally, the proposed multi-stage approach may also be applied topost-logarithm measurements, that is, where the input to thenormalization stage and the denoised denormalized blended output in FIG.2A are in the logarithm-domain. Taking the logarithm naturallylinearizes the pre-logarithm measurements, which may make it easier todesign the normalization and DL denoisers in the proposed multi-stageapproach. Moreover, one or more additional transforms that compress thedynamic range or adjust the histogram of the measurements (such as thelogarithm, power function, inverse tangent function), can be applied tothe input to the normalization stage, making the transformedmeasurements more suitable for further processing by the DL network.These additional transforms may be un-done during the de-normalizationstep.

Additionally, a mean-preserving correction step may be included as partof the blending stage to ensure that the final denoised denormalizedblended data exhibit local mean characteristics similar to the measureddata. This mean-preserving correction can be implemented using simpleimage-processing filters that operate on the difference between themeasured data and the denoised denormalized blended data. Preserving thelocal mean is essential to avoid low-frequency bias shifts in the finalreconstructed images which otherwise may affect quantitative accuracy ofthe final reconstructed images.

Although the above techniques were discussed with regard to emissiontomography data, it may also be utilized on magnetic resonance imagingdata or any data acquired in image applications that encounter highdynamic range measurements such as fluorescence microscopy and astronomyimaging.

FIG. 5 illustrates a schematic diagram of an encoder-decoder DL network64 for denoising. As depicted, the encoder-decoder DL network 64includes a plurality of stages 66 and a plurality of skip connections 68for bypassing certain stages 66. Each stage 66 may include differenttypes of convolutional layers 70 (e.g., 2D or 3D to 2D convolutionallayers), batch normalization layers 72, and/or activation layers74(e.g., rectified liner unit (ReLU) layers) 74.

The network 64 was utilized to demonstrate the techniques describedabove by simulating noisy 3D projection data and corresponding training“ground-truth” laves based on nearly noise-free projection data obtainedby averaging 100 scans of a subject (e.g., turkey meat from thebutcher). The normalized version of the noisy projections obtained asP_(n)=P/P_(d) were provided as input 76 to the network 64, where Pa wasobtained by smoothing P using a 11×11 boxcar filter. For every denoisedprojection view to be estimated, three views (including the view to bedenoised) were fed into the network 64. FIGS. 6A and 6B illustratetraining samples provided to the network 64. FIG. 6A depicts a stack ofcentral slices of 41×41×3-sized 3D blocks of noisy training samples 78(i.e., projection data) and FIG. 6B depicts the corresponding noise-freeground truth labels 80. The edges in normalized ground-truth labels 80are visible. The training samples were obtained using an average of theprojection data of the subject. The training was carried out to outputfrom the network 64 residual noise 82 in the noisy training input (viaresidual learning).

The loss function was chosen to be log(1+|DL_(output)−Label|) to addressthe presence of heavy-tailed noise in the normalized training samples.The evolution of the training and validation loss over epochs isillustrated in FIG. 7 . FIG. 7 includes a representation of a graph 84having an x-axis 86 for loss (in root mean square error (RMSE)) and ay-axis 88 for number of epochs. Plot 90 (shown as solid) represents thetraining loss and plot 92 (shown as a dashed) represents the validationloss. As depicted in FIG. 7 , the reduction of the validation loss overthe epochs indicate that the network 64 has trained without overfittingthe training data. For the post-processing stage, the DL-denoised outputand the original projections were combined using a simple linearcombination with weights w(P) decided by the signal strength in theoriginal measurements P.

FIGS. 8-10 illustrate the efficacy of the proposed DL-based LSC andsinogram denoising method. FIGS. 8A and 8B depict reconstructed axialimages 94, 96 of a shoulder region of a subject. Image 94 wasreconstructed without the proposed DL-based method for LSC, while image96 was reconstructed with the proposed DL-based method for LSC. Asdepicted in comparing FIGS. 8A and 8B, the strong streaks due to heavyattenuation in the shoulder region (in presence of some contrast agent)are greatly reduced illustrating the LSC-capability of the proposedmethod. FIGS. 9A and 9B depict reconstructed axial images 98, 100 of apelvic region of a subject. Image 98 was reconstructed without theproposed DL-based sinogram denoising method, while image 100 wasreconstructed with the proposed DL-based sinogram denoising method.FIGS. 10A and 10B depict reconstructed coronal images 102, 104 of anabdominal region of a subject. Image 102 was reconstructed without theproposed DL-based sinogram denoising method, while image 104 wasreconstructed with the proposed DL-based sinogram denoising method.Sinogram denoising using the proposed method reduces streaks in theaxial plane in the pelvic region (as shown in comparing FIGS. 9A and 9B)and also reduces noise throughout the whole volume (as shown incomparing FIGS. 10A and 10B).

Technical effects of the disclosed embodiments include providing athree-stage approach for reducing a dynamic range of data (e.g.,projection data or image data) to enable deep learning based denoisingand/or low signal correction. In particular, data may be normalized toenable a trained deep learning network to denoise the normalized data.The denoised data may then be denormalized and utilized to generateartifact-free and noise-free images with an image texture preferred bypractitioners.

This written description uses examples to disclose the present subjectmatter, including the best mode, and also to enable any person skilledin the art to practice the present approaches, including making andusing any devices or systems and performing any incorporated methods.The patentable scope is defined by the claims, and may include otherexamples that occur to those skilled in the art. Such other examples areintended to be within the scope of the claims if they have structuralelements that do not differ from the literal language of the claims, orif they include equivalent structural elements with insubstantialdifferences from the literal languages of the claims.

The invention claimed is:
 1. A computer-implemented method for imageprocessing, comprising: obtaining data acquired by a medical imagingsystem; normalizing the data; de-noising the normalized data utilizing adeep learning-based denoising network; de-normalizing the de-noiseddata; and generating blended data based on both the data and thede-normalized de-noised data.
 2. The computer-implemented method ofclaim 1, comprising generating a final reconstructed image from theblended data.
 3. The computer-implemented method of claim 1, wherein thedata comprises image data.
 4. The computer-implemented method of claim1, wherein the data comprises emission tomography data and magneticresonance imaging data.
 5. The computer-implemented method of claim 1,wherein the data comprises X-ray data.
 6. The computer-implementedmethod of claim 5, wherein the X-ray data comprises computed tomography(CT) scan data from single-energy CT, dual-energy CT, or multi-energyspectral CT.
 7. The computer-implemented method of claim 1, wherein theX-ray data comprises projection data.
 8. The computer-implemented methodof claim 7, wherein normalizing the data comprises normalizingpost-logarithm projection data.
 9. The computer-implemented method ofclaim 7, wherein normalizing the data comprises normalizingpre-logarithm projection data to reduce a dynamic range of thepre-logarithm projection data.
 10. The computer-implemented method ofclaim 1, wherein generating blended data based on both the data and thede-normalized de-noised data comprises applying weights to the data andthe de-normalized de-noised data to determine a respective contributionof the data and the de-normalized de-noised data to the blended data.11. The computer-implemented method of claim 10, wherein applying theweights to the data and the de-normalized de-noised data is based on anestimated signal-to-noise ratio of the data, an estimated noise level ofthe data, or a signal strength in the de-normalized de-noised data. 12.The computer-implemented method of claim 1, wherein generating blendeddata based on both the data and the de-normalized de-noised dataadditionally comprises a filtering operation to keep the local mean inthe de-normalized de-noised blended data the same as that in themeasured data.
 13. The computer-implemented method of claim 1, whereinnormalizing the data comprises generating and utilizing a noise-reducedversion of the data to obtain the normalized data.
 14. One or morenon-transitory computer-readable media encoding one or moreprocessor-executable routines, wherein the one or more routines, whenexecuted by a processor, cause acts to be performed comprising:obtaining projection data acquired by a computed tomography (CT) imagingsystem; normalizing the projection data; de-noising the normalizedprojection data utilizing a deep learning-based denoising network;de-normalizing the de-noised projection data; and generating blendedprojection data based on both the projection data and the de-normalizedde-noised projection data.
 15. The one or more non-transitorycomputer-readable media of claim 14, wherein the one or more routines,when executed by a processor, cause acts to be performed comprisinggenerating a final reconstructed image from the blended data.
 16. Theone or more non-transitory computer-readable media of claim 14, whereinnormalizing the data comprises normalizing post-logarithm projectiondata.
 17. The one or more non-transitory computer-readable media ofclaim 14, wherein normalizing the data comprises normalizingpre-logarithm projection data to reduce a dynamic range of thepre-logarithm projection data.
 18. The one or more non-transitorycomputer-readable media of claim 14, wherein generating blendedprojection data based on both the projection data and the de-normalizedde-noised projection data comprises applying weights to the projectiondata and the de-normalized de-noised projection data to determine arespective contribution of the projection data and the de-normalizedde-noised projection data to the blended projection data.
 19. The one ormore non-transitory computer-readable media of claim 14, whereingenerating blended projection data based on both the projection data andthe de-normalized de-noised projection data additionally comprises afiltering operation to keep the local mean in the de-normalizedde-noised blended data the same as that in the measured data.
 20. Aprocessor-based system, comprising: a memory structure encoding one ormore processor-executable routines, wherein the routines, when executedcause acts to be performed comprising: obtaining pre-logarithmprojection data acquired by a computed tomography (CT) imaging system;normalizing the pre-logarithm projection data to reduce a dynamic rangeof the pre-logarithm projection data; de-noising the normalizedprojection data utilizing a deep learning-based denoising network;de-normalizing the de-noised projection data; and generating blendedprojection data based on both the pre-logarithm projection data and thede-normalized de-noised projection data; and a processing componentconfigured to access and execute the one or more routines encoded by thememory structure.